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A method is being developed that provides for an artificial-intelligence system to learn a user's preferences for sets of objects and to thereafter automatically select subsets of objects according to those preferences. The method was originally intended to enable automated selection, from among large sets of images acquired by instruments aboard spacecraft, of image subsets considered to be scientifically valuable enough to justify use of limited communication resources for transmission to Earth. The method is also applicable to other sets of objects: examples of sets of objects considered in the development of the method include food menus, radio-station music playlists, and assortments of colored blocks for creating mosaics. The method does not require the user to perform the often-difficult task of quantitatively specifying preferences; instead, the user provides examples of preferred sets of objects. This method goes beyond related prior artificial-intelligence methods for learning which individual items are preferred by the user: this method supports a concept of setbased preferences, which include not only preferences for individual items but also preferences regarding types and degrees of diversity of items in a set. Consideration of diversity in this method involves recognition that members of a set may interact with each other in the sense that when considered together, they may be regarded as being complementary, redundant, or incompatible to various degrees. The effects of such interactions are loosely summarized in the term portfolio effect. The learning method relies on a preference representation language, denoted DD-PREF, to express set-based preferences. In DD-PREF, a preference is represented by a tuple that includes quality (depth) functions to estimate how desired a specific value is, weights for each feature preference, the desired diversity of feature values, and the relative importance of diversity versus depth. The system applies statistical concepts to estimate quantitative measures of the user s preferences from training examples (preferred subsets) specified by the user. Once preferences have been learned, the system uses those preferences to select preferred subsets from new sets. The method was found to be viable when tested in computational experiments on menus, music playlists, and rover images. Contemplated future development efforts include further tests on more diverse sets and development of a sub-method for (a) estimating the parameter that represents the relative importance of diversity versus depth, and (b) incorporating background knowledge about the nature of quality functions, which are special functions that specify depth preferences for features.
A flexible fuzzy set preference model first requires approximate methodologies for implementation. Fuzzy sets must be defined for each individual consumer using computer software, requiring a minimum of time and expertise on the part of the consumer. The amount of information needed in defining sets must also be established. The model itself must adapt fully to the subject's choice of attributes (vague or precise), attribute levels, and importance weights. The resulting individual-level model should be fully adapted to each consumer. The methodologies needed to develop this model will be equally useful in a new generation of intelligent systems which interact with ordinary consumers, controlling electronic devices through fuzzy expert systems or making recommendations based on a variety of inputs. The power of personal computers and their acceptance by consumers has yet to be fully utilized to create interactive knowledge systems that fully adapt their function to the user. Understanding individual consumer preferences is critical to the design of new products and the estimation of demand (market share) for existing products, which in turn is an input to management systems concerned with production and distribution. The question of what to make, for whom to make it and how much to make requires an understanding of the customer's preferences and the trade-offs that exist between alternatives. Conjoint analysis is a widely used methodology which de-composes an overall preference for an object into a combination of preferences for its constituent parts (attributes such as taste and price), which are combined using an appropriate combination function. Preferences are often expressed using linguistic terms which cannot be represented in conjoint models. Current models are also not implemented an individual level, making it difficult to reach meaningful conclusions about the cause of an individual's behavior from an aggregate model. The combination of complex aggregate models and vague linguistic preferences has greatly limited the usefulness and predictive validity of existing preference models. A fuzzy set preference model that uses linguistic variables and a fully interactive implementation should be able to simultaneously address these issues and substantially improve the accuracy of demand estimates. The parallel implementation of crisp and fuzzy conjoint models using identical data not only validates the fuzzy set model but also provides an opportunity to assess the impact of fuzzy set definitions and individual attribute choices implemented in the interactive methodology developed in this research. The generalized experimental tools needed for conjoint models can also be applied to many other types of intelligent systems.
Consumer preference models are widely used in new product design, marketing management, pricing, and market segmentation. The success of new products depends on accurate market share prediction and design decisions based on consumer preferences. The vague linguistic nature of consumer preferences and product attributes, combined with the substantial differences between individuals, creates a formidable challenge to marketing models. The most widely used methodology is conjoint analysis. Conjoint models, as currently implemented, represent linguistic preferences as ratio or interval-scaled numbers, use only numeric product attributes, and require aggregation of individuals for estimation purposes. It is not surprising that these models are costly to implement, are inflexible, and have a predictive validity that is not substantially better than chance. This affects the accuracy of market share estimates. A fuzzy set preference model can easily represent linguistic variables either in consumer preferences or product attributes with minimal measurement requirements (ordinal scales), while still estimating overall preferences suitable for market share prediction. This approach results in flexible individual-level conjoint models which can provide more accurate market share estimates from a smaller number of more meaningful consumer ratings. Fuzzy sets can be incorporated within existing preference model structures, such as a linear combination, using the techniques developed for conjoint analysis and market share estimation. The purpose of this article is to develop and fully test a fuzzy set preference model which can represent linguistic variables in individual-level models implemented in parallel with existing conjoint models. The potential improvements in market share prediction and predictive validity can substantially improve management decisions about what to make (product design), for whom to make it (market segmentation), and how much to make (market share prediction).
A number of reasoning problems involving the manipulation of temporal information can naturally be viewed as implicitly inducing an ordering of potential local decisions involving time (specifically, associated with durations or orderings of events) on the basis of preferences. For example. a pair of events might be constrained to occur in a certain order, and, in addition. it might be preferable that the delay between them be as large, or as small, as possible. This paper explores problems in which a set of temporal constraints is specified, where each constraint is associated with preference criteria for making local decisions about the events involved in the constraint, and a reasoner must infer a complete solution to the problem such that, to the extent possible, these local preferences are met in the best way. A constraint framework for reasoning about time is generalized to allow for preferences over event distances and durations, and we study the complexity of solving problems in the resulting formalism. It is shown that while in general such problems are NP-hard, some restrictions on the shape of the preference functions, and on the structure of the preference set, can be enforced to achieve tractability. In these cases, a simple generalization of a single-source shortest path algorithm can be used to compute a globally preferred solution in polynomial time.
Long-duration exploration missions will require a sustainable supply of food to support human crews. The spaceflight environment contains high concentrations of CO 2 due to release by astronauts that cannot be completely scrubbed. It is therefore crucial to understand plant responses to elevated CO 2 (eCO 2 ) environments. Nitrogen (N) is crucial for plant survival, though the effects of eCO 2 on plant N uptake remain poorly understood. Shoot nitrate reduction may be reduced at eCO 2 , due to reduced reductant availability for nitrate reduction due to reduced photorespiration and increased carbon fixation. Relative growth rate may be reduced at eCO 2 when N is provided only as nitrate and can be unaffected when N is supplied as ammonium, suggesting eCO 2 may drive increased plant ‘preference’ for ammonium. However, changes in N preference in response to eCO 2 have, to our knowledge, not yet been studied. In this study, novel stable isotope approaches were used in conjunction with hydroponics and isotope ratio mass spectrometry to determine the effect of space station-like eCO 2 (3000 ppm) on preference for ammonium or nitrate in several lettuce varieties previously grown in space, when both N forms are provided equally. All varieties displayed significant ammonium preference irrespective of CO 2 concentration, however the extent of this preference varied. Increased ammonium preference was observed for all varieties at eCO 2 compared to ambient CO 2 (410 ppm), driven by increases in nitrogen uptake which plants disproportionately took up as ammonium. These results suggest that future nutrient formulations should favor ammonium as a major N source for space crop production. Moreover, increased ammonium preference may play a role in future plant-based bioregenerative life support systems with higher ammonium concentrations due to waste recycling. Additionally, this research furthers our understanding of plant responses to future high CO 2 climates, allowing the development of future-proof crops to maintain food security.
A temporal reasoning problem can often be naturally characterized as a collection of constraints with associated local preferences for times that make up the admissible values for those constraints. Globally preferred solutions to such problems emerge as a result of well-defined operations that compose and order temporal assignments. The overall objective of this work is a characterization of different notions of global preference, and to identify tractable sub-classes of temporal reasoning problems incorporating these notions. This paper extends previous results by refining the class of useful notions of global temporal preference that are associated with problems that admit of tractable solution techniques. This paper also answers the hitherto open question of whether problems that seek solutions that are globally preferred from a Utilitarian criterion for global preference can be found tractably.
We have studied the chemistry of aminoacyl AMP to model reactions at the 3' terminus of aminoacyl tRNA for the purpose of understanding the origin of protein synthesis. The present studies relate to the D, L preference in the esterification of 5'-AMP. All N-acetyl amino acids we studied showed faster reaction of the D-isomer, with a generally decreasing preference for D-isomer as the hydrophobicity of the amino acid decreased. The beta-branched amino acids, Ile and Val, showed an extreme preference for D-isomer. Ac-Leu, the gamma-branched amino acid, showed a slightly low D/L ratio relative to its hydrophobicity. The molecular basis for these preferences for D-isomer is understandable in the light of our previous studies and seems to be due to preferential hydrophobic interaction of the D-isomer with adenine. The preference for hydrophobic D-amino acids can be decreased by addition of an organic solvent to the reaction medium. Conversely, peptidylation with Ac-PhePhe shows a preference for the LL isomer over the DD isomer.
Often we need to work in scenarios where events happen over time and preferences are associated to event distances and durations. Soft temporal constraints allow one to describe in a natural way problems arising in such scenarios. In general, solving soft temporal problems require exponential time in the worst case, but there are interesting subclasses of problems which are polynomially solvable. In this paper we identify one of such subclasses giving tractability results. Moreover, we describe two solvers for this class of soft temporal problems, and we show some experimental results. The random generator used to build the problems on which tests are performed is also described. We also compare the two solvers highlighting the tradeoff between performance and robustness. Sometimes, however, temporal local preferences are difficult to set, and it may be easier instead to associate preferences to some complete solutions of the problem. To model everything in a uniform way via local preferences only, and also to take advantage of the existing constraint solvers which exploit only local preferences, we show that machine learning techniques can be useful in this respect. In particular, we present a learning module based on a gradient descent technique which induces local temporal preferences from global ones. We also show the behavior of the learning module on randomly-generated examples.
Future long-duration missions will require a sustainable supply of food to support human crews. The spaceflight cabin environment often contains very high concentrations of CO 2 due to release of CO 2 by astronauts that is not completely scrubbed from the cabin, and it is therefore crucial to understand plant responses to elevated CO 2 (eCO 2 ) environments. Much focus has been given to changes in plant photosynthetic and performance parameters in response to eCO 2 , but the effects of eCO 2 on nitrogen (N) uptake are poorly understood. Shoot nitrate reduction may be reduced at eCO 2 , likely due to less reductant available for nitrate reduction because of reduced photorespiration and increased carbon fixation 1. Relative growth rate can be reduced at eCO 2 when N is provided only as nitrate and can be unaffected when N is supplied as ammonium 1. However, N uptake in response to eCO 2 has, to our knowledge, not yet been studied. An increased ‘preference’ for plants to take up N as ammonium at eCO 2 could have important implications for growth in the space environment, where N is currently only supplied as nitrate. In this study, novel stable isotope approaches were used in conjunction with hydroponics and isotope ratio mass spectrometry to determine the effect of eCO 2 on N preference for ammonium or nitrate in spring barley and lettuce when both N forms are provided equally. Several varieties of spring barley displayed increased ammonium preference at eCO 2 (720 ppm) compared to ambient CO 2 (410 ppm), though this was not true for all varieties 2. In most cases, increases in ammonium preference were driven by increases in ammonium uptake at eCO 2 and not decreases in nitrate uptake. Current research is assessing whether similar responses are observed in the candidate space crop lettuce, at levels of CO 2 like those observed on ISS (3000 ppm), and these results will also be presented. This work will enable the development of optimized nutrient regimes for candidate crops in space environments and the selection of crop varieties adapted to eCO 2 environments. Plants adapted to ammonium nutrition may play a role in future plant-based bioregenerative life support systems with higher ammonium concentrations due to waste recycling 3. Moreover, this research will further our understanding of plant responses to the eCO 2 environment brought about by climate change, allowing the development of future-proof crops that will help to maintain food security. References: 1. Bloom (2015). The increasing importance of distinguishing among plant nitrogen sources. Current Opinion in Plant Biology 25, 10-16. 2. Fountain (2023). Understanding interactions of barley (Hordeum vulgare) with soil nitrogen cycling activity and links to plant nitrogen preference. Ph.D. Thesis, The University of Sheffield. 3. Schiefloe et al. (2023). From urine to food and oxygen: effects of high and low NH4+:NO3- ratio on lettuce cultivated in a gas-tight hydroponic facility. Frontiers in Plant Science 14:1229476.
Rats were chronically centrifuged in excess of 2.0 g for 6 or 12 mo. They were given four 24-hr gravity-preference tests in a spiral centrifuge in which they could adjust the gravity level imposed by locomoting inward or outward radially along a track. Chronically centrifuged rats (Group CC) spent as much time at 2.0 g as at 1.0 g while normally raised controls (Group NC) selecdonly 1.0 g. Group CC initially selected 2.0 g and a preference for 1.0 g developed over the four test sessions. These results suggest that hypergravity is not necessarily an aversive stimulus and that gravity preference may depend initially upon the reference level involved. The ultimate selection of 1.0 g by chronically centrifuged animals suggests that a preference for a familiar gravity environment is replaced by a preference for low-gravity stimuli.
A survey was conducted to determine the opinions of readers concerning the format (organization) of NASA technical reports and usage of technical report components. A survey questionnaire was sent to 513 LaRC engineers and scientists and 600 engineers and scientists from three (3) professional/technical societies. The response rates were 74 and 85 percent, respectively. The questionnaire included the order in which users read report components, the components reviewed or read to determine whether to read a report, report components which could be deleted, the desirability of a table of contents, the desirability of both a summary and abstract, the location of the symbols list and glossary, the integration of illustrative material, the preferred format for reference citations, column layout and right margin treatment, and person/voice. The results of the reader preference survey indicated that the conclusion was the component most often ready by survey respondents. The summary, conclusion, abstract, title page, and introduction were the components used most frequently to determine if a report would actually be read. Respondents indicated that a summary as well as an abstract should be included, that the definition of symbols and glossary of terms should be located in the front of the report, and that illustrative material should be integrated with the text rather than grouped at the end of the report. Citation by number was the preferred format for references. A one-column, ragged right margin was preferred. Third person, passive voice was the style of writing preferred by the respondents.
A plotting of inverse pole figures is used to characterize the preferred orientations in both powder-extruded and cast-and-extruded NiAl, FeAl, and Ni3Al; the preferred 111 orientation has been noted along the extrusion direction in both powder-extruded and cast-and-extruded NiAl. While powder-extruded FeAl also had a 111 preferred orientation in the as-extruded condition, 110 was observed upon casting and extrusion; this was in turn replaced by a 211 orientation preference upon annealing, although annealing did not change the preferred orientation in either NiAl or in powder-extruded FeAl.
Future long-duration missions will require a sustainable supply of food to support human crews. The spaceflight cabin environment often contains very high concentrations of CO 2 due to release of CO 2 by astronauts that is not completely scrubbed from the cabin, and it is therefore crucial to understand plant responses to elevated CO 2 (eCO 2 ) environments. Much focus has been given to changes in plant photosynthetic and performance parameters in response to eCO 2 , but the effects of eCO 2 on nitrogen (N) uptake are poorly understood. Shoot nitrate reduction may be reduced at eCO 2 , likely due to less reductant available for nitrate reduction because of reduced photorespiration and increased carbon fixation 1. Relative growth rate can be reduced at eCO 2 when N is provided only as nitrate and can be unaffected when N is supplied as ammonium. However, N uptake in response to eCO 2 has, to our knowledge, not yet been studied. An increased ‘preference’ for plants to take up N as ammonium at eCO 2 could have important implications for growth in the space environment, where N is currently only supplied as nitrate. In this study, novel stable isotope approaches were used in conjunction with hydroponics and isotope ratio mass spectrometry to determine the effect of eCO 2 on N preference for ammonium or nitrate in spring barley and lettuce when both N forms are provided equally. Several varieties of spring barley displayed increased ammonium preference at eCO 2 (720 ppm) compared to ambient CO 2 (410 ppm), though this was not true for all varieties. In most cases, increases in ammonium preference were driven by increases in ammonium uptake at eCO 2 and not decreases in nitrate uptake. Current research is assessing whether similar responses are observed in the candidate space crop lettuce, at levels of CO 2 like those observed on ISS (3000 ppm), and these results will also be presented. This work will enable the development of optimized nutrient regimes for candidate crops in space environments and the selection of crop varieties adapted to eCO 2 environments. Plants adapted to ammonium nutrition may play a role in future plant-based bioregenerative life support systems with higher ammonium concentrations due to waste recycling. Moreover, this research will further our understanding of plant responses to the eCO 2 environment brought about by climate change, allowing the development of future-proof crops that will help to maintain food security.
Hand preferences were recorded for 35 rhesus monkeys (Macaca mulatta) as they manipulated a joystick in response to 2 computerized tasks. These preferences were then used to contrast 8 left- and 10 right-handed subjects on performance measures of hand skill. Individual hand preferences were found, but no significant population asymmetry was observed across the sample. However, the performance data reveal substantial benefits of right-handedness for joystick manipulation, as this group of monkeys mastered the 2 psychomotor tasks significantly faster than did their left-handed counterparts. The data support earlier reports of a right-hand advantage for joystick manipulation and also support the importance of distinguishing between hand preference and manual performance in research on functional asymmetries.
Ten years of stratospheric geopotential height data are analyzed in an attempt to determine whether there are preferred flow regimes in the Northern Hemisphere winter stratosphere. The data are taken from Stratospheric Sounding Units on board NOAA satellites. The probability density estimate of the amplitude of the wavenumber 1 10-mb height is found to be bimodal. The density distribution is composed of a dominant large-amplitude mode and a less frequent low-amplitude mode. When the wavenumber 1 10-mb height data are projected onto the phase plane defined by the 10-mb zonal-mean winds and wavenumber 1 100-mb heights, three preferred regimes are evident. The small-amplitude mode separates into a strong zonal wind-weak wave regime and a weak zonal wind-weak wave regime. The large-amplitude mode is an intermediate zonal wind-strong wave regime. Transitions between the large-amplitude regime and the weak zonal wind-weak wave regime are found to be associated with major stratospheric warmings. The clustering of the stratospheric data into the preferred flow regimes is interpreted in light of the bifurcation properties of the Holton and Mass model. The interannual variability of the Northern Hemisphere winter stratosphere is interpreted in terms of the relative frequency of the observed preferred regimes.
The latitude preference of the intertropical convergence zone (ITCZ) is examined on the basis of observations, theory, and a modeling analysis. Observations show that convection is enhanced at latitudes of about 4 deg to 10 deg relative to the equator, even in regions where the sea surface temperature (SST) is maximum on the equator. Both linear shallow-water theory and a moist primitive equation model suggest a new explanation for the off-equatorial latitude preference of the ITCZ that requires neither the existence of zonally propagating disturbances nor an off-equatorial maximum in SST. The shallow-water theory indicates that a finite-width, zonally oriented, midtropospheric heat source (i.e., an ITCZ) produces the greatest local low-level convergence when placed a finite distance away from the equator. This result suggests that an ITCZ is most likely to be supported via low-level convergence of moist energy when located at these "preferred" latitudes away from the equator. For a plausible range of heating widths and damping parameters, the theoretically predicted latitude is approximately equal to the observed position (s) of the ITCZ (s). Analysis with an axially symmetric, moist, primitive equation model indicates that when the latent heating field is allowed to be determined internally, a positive feedback develops between the midtropospheric latent heating and the low-level convergence, with the effect of enhancing the organization of convection at latitudes of about 4 deg to 12 deg. Numerical experiments show that (1) two peaks in convective precipitation develop straddling the equator when the SST maximum is located on the equator; (2) steady ITCZ-like structures form only when the SST maximum is located away from the equator; and (3) peaks in convection can develop away from the maximum in SST, with a particular preference for latitudes of about 4 deg to 12 deg, even in the ('cold') hemisphere without the SST maximum. The relationship between this mechanism and earlier theories is discussed, as are implications for the coupled ocean-atmosphere system and the roles played by midlevel latent heating and SST gradients in forcing the low-level atmospheric circulation in the tropics.
This experiment assessed certain issues about the usefulness of predictive information: (1) the relative time criticality of failures, (2) the subjective utility of predictive information for different parameters or sensors, and (3) the preferred form and prediction time for displaying predictive information. To address these issues, three separate tasks were administered to 22 airline pilots. As shown by the data, these pilots preferred predictive information on parameters they considered vital to the safety of the flight. These parameters were related to the checklists performed first for alert messages. These pilots also preferred to know whether a parameter was changing abnormally and the time to a certain value being reached. Furthermore, they considered this information most useful during the cruise, the climb, and the descent phases of flight. Lastly, these pilots preferred the information to predict as far ahead as possible.